Effect of Antenna Configuration on MIMO-Based Access Points in a Short Tunnel With Infrastructure
Bibliographic record
Abstract
Performance of MIMO technology is highly dependent on the surrounding propagation environment and antenna configuration. In order to benefit from MIMO technology inside underground mines with low angular spread, careful antenna design and deployment strategies are required. In this regard, we studied the effect of uniform-linear-array (ULA) configuration on the MIMO channel capacity inside a short underground service tunnel. With its extensive infrastructure, this tunnel represents a typical midsize underground-mine tunnel. We simulated and measured channel frequency response at 2.49 GHz for grids of transmitters and receivers in different parts of the tunnel while taking into account practical considerations for access point (AP) deployments. All the ULA configurations were deployed either close to the sidewalls or under the ceiling. Among them, two configurations, which show the best performance for AP communications, were identified. For these configurations, we found that interelement separation requires to be four times longer than in conventional indoor environment with rich multipath (i.e., 2λ) to provide decorrelation among MIMO subchannels. We also found that infrastructure which has been neglected in previous studies, significantly degrades MIMO performance. Insightful results and guidelines for employing MIMO technology in underground mines can significantly benefit mining industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".